The adoption of superposition multiple access is a recent development in the 3rd Generation Partnership Project (3GPP). See, e.g., Chairman's Notes, 3GPP RAN1 Meeting #80b, Belgrade (2014 Apr. 20). Although often referred to in 3GPP as Multi-User Superposition Transmission (MUST), superposition multiple access techniques has various names and various types, including, and not limited to, Non-Orthogonal Multiple Access (NOMA), Semi-Orthogonal Multiple Access (SOMA), Rate-adaptive constellation Expansion Multiple Access (EMA), Downlink Multiple User (DL MU), etc. The present disclosure is not limited to any of the afore-mentioned technologies, but has wide applicability to any superposition communication technology.
In general, multiple access superposition refers to communicating to multiple users by linearly combining amplitude-weighted, encoded, and/or modulated messages. For example, FIG. 1 has Base Station (BS) 110 (or evolved NodeB (eNB)) and two users (or User Equipments (UEs)), a near UE 120 and a far UE 130 (“near” and “far” referring to their relative distances from BS 110). Both the near UE 120 and the far UE 130 receive the same signal x, comprising symbol xn for the near UE 120 and symbol xf for far UE 130, which can be represented by Equation (1):x=√{square root over (αN)}xn+√{square root over (αF)}xF  (1)
where α generally refers to transmission power, and thus αN is the transmission power allocated to the near user signal and αF is the transmission power allocated to the far user, where αN+αF=1. Sometimes a refers more generally to the ratio of near user power to far user power, as shown in FIG. 2, which is discussed further below.
Speaking simplistically, near UE 120 decodes symbol xf for far UE 130 and uses it to cancel xf as interference, thereby decoding symbol xn intended for the near UE 120. One reiterative process for this type of cancellation is “Successive Interference Cancellation” or SIC. The far UE 130, on the other hand, simply decodes its own signal xf. (although it is possible for the far user to also perform some form of signal cancellation to eliminate xn).
Generally herein, far user symbol xF corresponds to KF bits of data represented as (d0Fd1F . . . dKF−1F) and near user symbol xN corresponds to KN bits of data represented as (d0Nd1N . . . dKN−1N).
FIG. 2 shows an example of a “super-constellation” formed of a (QPSK, QPSK) modulation pair under MUST. “(QPSK, QPSK)” means that both the far and near UE signals are modulated by QPSK. FIG. 2 is the result of a direct symbol mapping (DSM) of QPSK using Equation (1) for both the near and far users, i.e., a 16-QAM (Quadrature Amplitude Modulation) super-constellation. Moreover, in FIG. 2, the constituent xf and xn symbols are separately Gray encoded.
Each of the four bit symbols in the 16-QAM super-constellation in FIG. 2 comprises two bits for the symbol intended for the far user and two bits of the symbol intended for the near user. More specifically, each four-bit symbol (b0, b1, b2, b3) comprises (b0, b1)=(d0Fd1F), the two bits for the far user, and (b2, b3)=(d0Nd1N), the two bits for the near user. Thus, the far user constellation is relatively coarse, because each quadrant represents only one symbol (for example, the upper right quadrant is (00)), while each quadrant of the near user constellation has all four symbols (00, 01, 10, and 11). However, because the near user is nearer, the near user's received signal is stronger and it will be easier for the near user to distinguish that level of detail than the far user.
In theory, having the near user employ Successive Interference Cancellation (SIC) by codeword, where the far user codeword is decoded, the original encoded far user codeword reconstructed using the decoded codeword, and then the reconstructed original signal cancelled from the overall signal prior to decoding, is optimal in the sense that it achieves capacity.
In practice, Code Word Interference Cancellation (CWIC), as described above, is rather difficult because the near user receiver needs to have the far user's transmission parameters, such as, e.g., the codeword Modulation and Coding Scheme (MCS), precoding matrix, rank, power boost, etc. If, for example, the network provided this information it would lead to an increase in control signaling overhead. In addition, the decoding, re-constructing, and cancelling of the far user's codeword leads to a substantial usage of resources.
By contrast, Symbol-Level Interference Cancellation (SLIC) is a low-complexity approach and when joint detection, i.e., Maximum Likelihood (ML) detection, is used, SLIC can approach the performance of CWIC in many scenarios. However, when SLIC is used, the log-likelihood ratio (LLR) distribution of the different bits in both symbols xN and xF can affect performance. For example, the direct symbol mapping (DSM) leads to degraded SLIC performance.